Tumors are associated with the highest mortality rates worldwide. For more than a decade, research has focused on the genetic involvement of proteins in cancer; however, a complete class of molecular non‑coding (nc)RNAs have been discovered in recent years, and these are considered to be associated with cancer. Notably, ncRNAs are highly conserved and multifunctional. These interact with multiple signaling pathways, influencing cell cycle progression and various physiological processes. Therefore, the present review aimed to investigate ncRNA, microRNA, transfer RNA‑derived small RNA, PIWI‑interacting RNA and long non‑coding RNA to further understand the associated generation processes, functional mechanisms and therapeutic roles in tumors. The present review demonstrated the critical role of ncRNAs in tumors, and may provide a novel theoretical basis for the role of ncRNAs as biomarkers or therapeutic tools in the treatment of cancer.

In the pathogenesis of major depressive disorder, chronic stress-related neuroinflammation hinders favorable prognosis and antidepressant response. Mitochondrial DNA may be an inflammatory trigger, after its release from stress-induced dysfunctional central nervous system mitochondria into peripheral circulation. This evidence supports the potential use of peripheral mitochondrial DNA as a neuroinflammatory biomarker for the diagnosis and treatment of major depressive disorder. Herein, we critically review the neuroinflammation theory in major depressive disorder, providing compelling evidence that mitochondrial DNA release acts as a critical biological substrate, and that it constitutes the neuroinflammatory disease pathway. After its release, mitochondrial DNA can be carried in the exosomes and transported to extracellular spaces in the central nervous system and peripheral circulation. Detectable exosomes render encaged mitochondrial DNA relatively stable. This mitochondrial DNA in peripheral circulation can thus be directly detected in clinical practice. These characteristics illustrate the potential for mitochondrial DNA to serve as an innovative clinical biomarker and molecular treatment target for major depressive disorder. This review also highlights the future potential value of clinical applications combining mitochondrial DNA with a panel of other biomarkers, to improve diagnostic precision in major depressive disorder.

This paper reviews biomarkers in lysosomal disease according to their categories and definitions. There are numerous biomarkers in lysosomal diseases. Some are disease or organ-specific, but most are not. Organ-specific biomarkers are especially useful, but most biomarkers help with diagnosis, assessing disease severity, prognosis, and pharmacodynamic response. There are as yet no truly validated biomarkers in lysosomal diseases by the Prentice, Fleming, and DeMets criteria. None so far can serve as surrogate endpoints in clinical trials, or as substitutes for clinically meaningful endpoints that evaluate how patients feel, function, or survive. The US Food and Drug Administration has thus far used surrogate biomarkers to license therapy only for 3 lysosomal diseases-Gaucher disease, Fabry disease, and lysosomal lipase deficiency. The paucity of surrogate biomarkers reflects success in using clinically important endpoints for the licensing of therapies for Pompe disease, mucopolysaccharidosis IVA, VI, and VII, Niemann-Pick type C, and CLN2. In conclusion, biomarkers in lysosomal diseases are best used for diagnosis, patient categorization, pharmacodynamic response, and sometimes for patient prognosis and risk. Thus far, they have been less useful as surrogate biomarkers in pivotal clinical trials.

The regulatory landscape in rare diseases and oncology has evolved to address unmet medical needs by implementing expedited approval pathways. The US FDA's Accelerated Approval and the EMA's Conditional Marketing Authorization facilitate earlier patient access to therapies through reliance on surrogate endpoints derived from early-phase clinical trials. The review aims to provide a comprehensive review of the role and utilization of surrogate endpoints in accelerated drug approvals, highlighting their strengths, limitations, and the varying perspectives of stakeholders on their validity and utility.

This article reviews existing literature and regulatory guidelines to assess the effectiveness and challenges associated with surrogate endpoints in expedited approval pathways. It also examines the post-approval commitment adherence required by regulatory bodies, exploring discrepancies among stakeholder perspectives.

Findings indicate that while surrogate endpoints enable faster market access, uncertainties remain regarding post-approval commitments and their consistency. Differences in stakeholder opinions also persist, reflecting varying levels of confidence in the validity and applicability of surrogate endpoints.

Surrogate endpoints play a crucial role in accelerating drug approvals in areas with high unmet needs, yet challenges around post-approval commitments and stakeholder acceptance suggest the need for enhanced regulatory clarity and ongoing assessment of surrogate endpoint validity.

Parkinson's disease (PD) is a leading cause of disability worldwide, and there is a pressing need to develop therapeutics to slow or halt disease progression. The identification of reliable biomarkers of PD at all stages of disease will be a critical step toward optimizing diagnosis and therapeutic development. For PD, biomarkers could serve multiple important functions. There have been significant advances in biomarker development in PD in recent years, and in this review, the authors summarize the current state of the PD biomarker field covering major advances in fluid, tissue, and neuroimaging biomarkers.

Asthma is a common global respiratory disease characterized by airway inflammation. It is a heterogenous group of disorders with overlapping biological mechanisms. This review will discuss the current state of the use of biomarkers in asthma with an eye to the future. The identification of biomarkers has advanced our understanding of inflammatory pathways in asthma and aided in development of targeted therapies. However, even with similar inflammatory biomarkers, not all patients respond uniformly. Thus, further research into novel biomarkers in asthma is needed.

Recent literature highlights several key themes in biomarker research for asthma. Biomarkers can be derived from various sources, including sputum, blood, urine, and exhaled breath. Historically, studies have focused on eosinophilic inflammation, yet total blood eosinophil counts do not capture asthma pathology and treatment responses. Recent investigations explore eosinophil activity as well as eosinophil subpopulations based on surface protein expressions. Mast cell involvement, their mediators, and club cell secretory protein are further being examined across different asthma molecular phenotypes.

The complexity of inflammatory pathways in asthma, influenced by various factors, underscores the inadequacy of relying on a single biomarker at one time point. Continued research is essential to identify appropriate biomarkers.

Understanding the pathophysiology of traumatic brain injury (TBI) is crucial for effectively managing care. Diffusion tensor imaging (DTI) is an MRI technology that evaluates TBI pathology in brain white matter. However, DTI analysis generates numerous measures. Choosing between them remains an obstacle to clinical translation. In this study, we leveraged an iterative receiver operating characteristic (ROC) analysis to examine white matter tracts in a group of 380 Veterans, consisting of TBI (n = 243) and non-TBI patients (n = 137).

For each participant, we obtained a whole brain tractography and extracted DTI measures from 50 tracts. The ROC analyzed these variables and produced decision trees of tracts diagnostic for TBI. We expanded our findings by applying jackknife resampling. This procedure removed potential outliers and yielded tracts not observed in the initial ROCs. Finally, we used logistic regression to confirm the tracts predicted TBI status.

Our results indicate ROC can identify tracts diagnostic for TBI. We also found that groups of tracts are more predictive than any single one.

These analyses show that ROC is a useful tool for exploring large, multivariate datasets and may inform the design of clinical algorithms.

In this scoping review, we examine the role of wearable devices in diagnosing, treating, and monitoring Alzheimer's disease (AD) and mild cognitive impairment (MCI). It identifies various devices, including fitness trackers, smartwatches, electroencephalographic equipment, and sensors, which are used for monitoring physical activity, sleep patterns, and cognitive functions. Our review highlights the potential of these devices for early diagnosis and treatment, improving patient autonomy and quality of life. However, challenges, such as data privacy, device adherence, and technical limitations, remain. Future research should focus on integrating wearable devices with advanced diagnostic tools and validating their effectiveness across diverse populations.

Analysis of the blood proteome allows identification of proteins related to changes upon certain physiological conditions. The pathophysiology of necrotic enteritis (NE) has been extensively studied. While intestinal changes have been very well documented, data addressing NE-induced alterations in the blood proteome are scant, although these might have merit in diagnostics. In light of recent technological advancements in proteomics and pressing need for tools to access gut health, the current study employs mass-spectrometry (MS)-based proteomics to identify biomarkers for gastrointestinal health of chickens. Here, we report findings of an untargeted proteomics investigation conducted on blood plasma in chickens under NE challenge. Two MS-strategies were used for analysis: conventional data dependent acquisition coupled to standard nanoflow liquid chromatography (LC) (nano-DDA) and recently-developed data independent acquisition coupled to an Evosep One LC system (Evo-DIA). Despite superior completeness and quantification of the Evo-DIA-acquired data, high degree of agreement in identification and quantification was observed between both approaches. Additionally, we identified 15 differentially expressed proteins (shared by nano-DDA and Evo-DIA) that represent responses of animals to infection and may serve as potential biomarkers. Experimental validation through ELISA immunoassays and targeted MS for selected regulated proteins (CFD, HPS5, and MASP2) confirmed medium-to-high levels of inter-protein correlation. A GSEA analysis revealed enrichment in a number of processes related to adaptive and humoral immunity, immune activation and response in infected animals. Data are available via ProteomeXchange with identifiers PXD050461, PXD050473, and PXD061607.

(1) Background: The lack of reliable biomarkers remains a significant barrier to improving outcomes for patients with schizophrenia. While metabolomic analyses of blood, urine, and feces have been explored, results have been inconsistent. Compared to peripheral compartments, cerebrospinal fluid (CSF) more closely reflects the chemical composition of brain extracellular fluid. Given that brain dysregulation may be more pronounced during the first episode of psychosis (FEP), we hypothesized that metabolomic analysis of CSF from FEP patients could reveal disease-associated biomarkers. (2) Methods: We recruited 15 patients within 24 months of psychosis onset (DSM-4 criteria) and 14 control participants through the Johns Hopkins Schizophrenia Center. CSF samples were analyzed using both non-targeted and targeted liquid chromatography-mass spectrometry. (3) Results: The non-targeted analysis identified lower levels of N-acetylneuraminic acid and N-acetyl-L-aspartic acid in the FEP group, while levels of uric acid were elevated. The targeted analysis focused on indolic and phenolic molecules previously linked to neuropsychiatric disorders. Notably, L-phenylalanine and 4-hydroxycinnamic acid levels were lower in the FEP group, and this difference remained significant after adjusting for age and sex. However, none of the significant differences in analyte levels between the groups survived an adjustment for multiple comparisons. (4) Conclusions: Our intriguing but preliminary associations align with results from other investigational approaches and highlight potential CSF analytes that warrant further study in larger samples.

Psoriasis is an unexceptional autoimmune-mediated, inflammatory skin disorder impacting systemic skin functions. The pathophysiology of psoriasis comprises hyperproliferation of cells on the epidermis, differentiation of keratinocytes, and the impaired barrier function of the epidermal layer of the skin, developing in the thickening of the epidermal layer. From a range of inflammatory mediators concerned during the pathogenesis of psoriasis, IL-17, -23, and TNF-α exert a significant influence on the upregulation of the symptoms. There are diverse conventional approaches dealing with psoriasis, including topical, systemic, biological, and herbal formulations. The demand for innovative formulations has emerged as several adverse effects correspond to conventionally pre-existing formulations. As hyaluronic acid (HA) has manifold structural and functional characteristics that can be worthwhile in regulating the symptoms of multiple skin inflammatory conditions, it can be used in novel formulations to amplify therapeutic effectiveness and achieve enviable responses. Moreover, HA can also serve the role of a biomarker for psoriasis according to its molecular weight. Furthermore, the mechanistic role of HA in its native form can be advantageous in ameliorating the symptoms of psoriasis. This review unequivocally covers fundamental aspects and the latest advancements in HA-based formulations for mitigating psoriasis symptoms. Furthermore, we deliberated on the role of HA as a biomarker in the physiological system of humans, in accordance with its molecular weight, the rationale behind its selection, and its mechanistic role, and how HA profoundly augments the impactfulness of various formulations in eliciting a prominent therapeutic improvement and mitigating symptoms associated with this disease.

In order to improve illness identification, monitoring, and patient outcomes, this special report emphasizes the revolutionary potential of fluid and imaging biomarkers using new diagnostic technologies in Multiple system atrophy (MSA).

Innovations like multiplex seeding aggregation assays (SAA), 18FDG-Positron Emission Tomography (PET), and SPECT) are changing the diagnostic landscape. These techniques make it easier to detect MSA early and offer noninvasive monitoring choices. Although neurofilament light chain measurements in blood and cerebrospinal fluid (CSF), as well as α-synuclein-based diagnostic biomarkers in CSF, are recognized as both diagnostic and surrogatemarkers of disease progression in MSA, their application in clinical practiceis limited to research. Some efforts are being made in the development ofselective α- synucleinPET tracers despite numerous barriers in visualizing intracellular localization of α-synuclein. The primary drawbacks include the high expense of SAA and imaging technologies, the paucity of multicenter longitudinal investigations, and the lack of uniformity of the prοtocols. The research highlights that to successfully solve these restrictions, stakeholders must continue to collaborate.

A multi-dimensional biomarker system of MSA patients maximizes the power of contemporary diagnostics to enhance MSA care by prioritizing the ongoing evaluation of multi-omics data.

Biomarkers are frequently used in clinical practice; however, it is essential to consider the genetics that may independently impact their baseline levels in-turn impacting interpretation of results. For example, estimated glomerular filtration rate (eGFR) equations are based on two biomarkers, cystatin C and creatinine, and widely employed in clinical practice. In this work we demonstrate how genetics of the underlying biomarkers impact measurement variability and may explain some of the discrepancies among eGFR equations. To do this we used shared genetic-architecture to identify "shared" (renal-specific) and "biomarker-specific" regions of the genome. The shared polygenic risk score (PRS) explained 60% more of the variability in the most-common creatinine-derived eGFR estimates than either the biomarker-specific PRS or a PRS including all regions. Our findings highlight the necessity of considering biomarker-specific genetics when constructing PRS for eGFR and other biomarker-derived clinical risk estimates.

Antiseizure medication (ASM) induced metabolic syndrome (AIMetS) is a common adverse drug reaction (ADR) of pharmacotherapy for epilepsy and psychiatric disorders. However, the sensitivity and specificity of blood biomarkers may be insufficient due to the influence of combined pathology, concomitant diseases, and the peculiarities of the metabolism of ASMs in patients with epilepsy. Methods: The presented results of experimental and clinical studies of microRNAs (miRs) as epigenetic biomarkers of MetS and AIMetS, which were entered into the different databases, were analyzed for the last decade (2014-2024). Results: A systematic review demonstrated that miRs can act as promising epigenetic biomarkers of key AIMetS domains. However, the results of the review demonstrated the variable role of various miRs and their paralogs in the pathogenesis of AIMetS. Therefore, as part of this study, an miRs signature was proposed that allows us to assess the risk of developing and the severity of AIMetS as low risk, medium risk, and high risk. Conclusions: The mechanisms of development and biomarkers of AIMetS are an actual problem of epileptology, which is still far from being resolved. The development of panels (signatures) of epigenetic biomarkers of this widespread ADR may help to increase the safety of pharmacotherapy of epilepsy. However, to increase the sensitivity and specificity of circulating miRs in the blood as biomarkers of AIMetS, it is necessary to conduct "bridge" studies in order to replicate the results of preclinical and clinical studies into real clinical practice.

Mobile health applications are increasingly valued for their role in asthma management and the opportunity for large dataset collection. Our study aimed to investigate the feasibility of applying signal-processing and machine-learning technologies to detect alterations in the lower airway caliber and develop a machine-learning algorithm to identify changes in vocal biomarkers and detect bronchoconstriction in patients with airway hyperreactivity.

This is an explorative observational prospective longitudinal study focused on vocal biomarkers and their association with bronchial constriction and respiratory function. Non-smoker adults with clinical suspicion of asthma were consecutively enrolled from May 2023 to September 2023. At each step of a Methacholine Challenge Test (MCT) performed on these patients, the respiratory sounds were recorded via a smartphone through an app specifically developed. Several biomarkers were extracted and their relationship with the change in Forced Expiratory Volume in the first second (FEV1) was measured.

Forty-two subjects were enrolled. The highest correlation with FEV1 came from exhalation vocal events. No single feature exhibited robust behavior across different subjects, while each subject showed "personal" highly correlated features. All values were strongly statistically significant irrespectively of the result of MCT.

The app's algorithm is sensitive in correlating specific vocal biomarkers to FEV1 variations during MCT. This feature may assist physicians in diagnosing asthma and its exacerbation and in assessing therapy response and adherence. The socio-economic implications might be significant, and the simplicity of use makes it an ideal tool for research.

Bipolar disorder (BD) is a severe psychiatric condition whose pathophysiology is complex and multifactorial. Genetic, environmental and social risk factors play a role in its development as well as in its progressive course. Research is currently focusing on the identification of the biological basis underlying these processes in order to suggest novel biomarkers capable to predict BD etiopathogenesis and staging. Staging has been recognized as of great value for the treatment and management of many illnesses and might also be suitable for mental health issues, particularly in disorders like BD, which progress from an initial mild phase to a more severe and thus difficult-to-treat situation. Thus, it would be of great help the characterization of to suggest better treatment requirements and improve prognosis across the different stages of the illness. Here, we summarize current research on the biological hypotheses of BD and the biomarkers associated with its progression, reviewing clinical studies available in the literature.

Acute heart failure (AHF) classification and management are primarily based on lung congestion and/or hypoperfusion. The quantification of the vascular and tissue lung damage is not standard practice though biomarkers of lung injury may play a relevant role in this context. Haemodynamic stress promotes alveolar and vascular derangement with loss of functional units, impaired lung capillary permeability and fluid swelling. This culminates in a remodelling process with activation of inflammatory and cytokines pathways. Four families of lung surfactant proteins (i.e., SP-A, SP-B, SP-C, and SP-D), essential for the membrane biology and integrity are released by alveolar type II pneumocites. With deregulation of fluid handling and gas exchange pathways, SPs become sensitive markers of lung injury. We report the pathobiology of lung damage; the pathophysiological and clinical implications of alveolar SPs along with the newest evidence for some classical HF biomarkers that have also shown to reflect a vascular and/or a tissue lung-related activity.

Prognostic biomarkers have been widely studied in COVID-19, but their levels may be influenced by treatment strategies. This study examined plasma biomarkers and proteomic survival prediction in two unvaccinated hospitalized COVID-19 cohorts receiving different treatments. In a derivation cohort (n = 126) from early 2020, we performed plasma proteomic profiling and evaluated innate and complement system immune markers. A proteomic model based on differentially expressed proteins predicted 30-day mortality with an area under the curve (AUC) of 0.81. The model was tested in a validation cohort (n = 80) from late 2020, where patients received remdesivir and dexamethasone, and performed with an AUC of 0.75. Biomarker levels varied considerably between cohorts, sometimes in opposite directions, highlighting the impact of treatment regimens on biomarker expression. These findings underscore the need to account for treatment effects when developing prognostic models, as treatment differences may limit their generalizability across populations.

The diagnosis of periodontitis is primarily through clinical and radiographic assessments. However, it is difficult for clinicians to detect incipient periodontitis during the routine clinical assessment. Identifying people at risk for periodontitis and tracking disease development need a dependable biomarker. Currently, no biomarkers meet all the criteria required for an ideal diagnostic test. Therefore, the clinical utility of salivary periostin as a potential screening tool for periodontitis warrants further investigation, particularly through large samples across diverse populations. The present study aimed to investigate salivary periostin levels as a biomarker in individuals with periodontitis and healthy controls.

Forty-five patients with generalized periodontitis stage III grade A/B and an equivalent number of periodontally healthy controls were evaluated for plaque index (PI), gingival index (GI), pocket probing depth (PPD), and clinical attachment level (CAL). Unstimulated salivary samples from all subjects were taken, and periostin levels were quantified using an ELISA kit.

The average salivary periostin levels were 4.63 in the healthy group and 1.24 in the periodontitis group (P < 0.05). The Spearman coefficient indicated a negative correlation between periostin levels and the gingival index (r = -0.761), plaque index (r = -0.780; P < 0.05), probing pocket depth (PPD) (r = -0.713; P < 0.05) and clinical attachment level (CAL) (r = -0.713; P < 0.05). Linear regression analysis validated the indirect correlation between salivary periostin levels and clinical indicators (Adjusted R square = 0.947).

Salivary periostin levels are associated with periodontal disease. Salivary periostin levels indirectly influence as a non-invasive biomarker of periodontitis. The biomarker periostin is effective for evaluating both healthy and diseased periodontium.

Quantitative MRI has been an active area of research for decades and has produced a huge range of approaches with enormous potential for patient benefit. In many cases, however, there are challenges with reproducibility which have hampered clinical translation. Quantitative MRI is a form of measurement and like any other form of measurement it requires a supporting metrological framework to be fully consistent and compatible with the international system of units. This means not just expressing results in terms of seconds, meters, etc., but demonstrating consistency to their internationally recognized definitions. Such a framework for MRI is not yet complete, but a considerable amount of work has been done internationally towards building one. This article describes the current state of the art for MRI metrology, including a detailed description of metrological principles and how they are relevant to fully quantitative MRI. It also undertakes a gap analysis of where we are versus where we need to be to support reproducibility in MRI. It focusses particularly on the role and activities of national measurement institutes across the globe, illustrating the genuinely international and collaborative nature of the field.

Several biomarkers in peri-implant crevicular fluid have been studied to diagnose peri-implant diseases with inconclusive results. This systematic review and meta-analysis aimed to comprehensively compare data on the levels of biological components in peri-implant crevicular fluid collected from healthy and diseased implants to identify reliable biomarkers for diagnosing and monitoring peri-implant disease.

The search strategy included studies comparing biomarker levels in peri-implant crevicular fluid between healthy and diseased implants through electronic databases (MEDLINE/PubMed, Embase, Cochrane Library), grey literature, and hand-searching relevant journals and reference lists of pertinent papers. A two-stage screening was performed in duplicate and independently. In the first stage, titles and abstracts that fulfilled eligibility criteria were screened. In the second stage, a full-text analysis was conducted to verify eligibility. All articles meeting the inclusion criteria underwent data extraction and quality assessment. Meta-analyses were conducted on studies with similar comparisons and outcome measures.

After screening the titles and abstracts, out of 100 potentially relevant papers identified for full-text evaluation, 49 were excluded, 51 were included in the qualitative analysis, and 18 were included in the quantitative synthesis. Among 96 biomarkers assessed, the most studied were pro-inflammatory cytokines (IL-1ß, IL-6, TNF-α, and IL-17), osteoclastogenic-related factors (RANK, RANKL, and OPG), anti-inflammatory cytokines (IL-10), chemokines (IL-8, MIP-1α/CCL3, and MIP-3α/CCL-20), and enzymes (MMP-8, Cat-K, AST, and ALT).

Meta-analyses comparing data from healthy patients and those with peri-implantitis or mucositis and between patients with mucositis and those with peri-implantitis showed a moderate predictive value of IL-1ß, VEGF, cortisol, and sRANKL/OPG for peri-implantitis.

One of the main causes of morbidity and death in pediatric patients is sepsis. Of the 48.9 million cases of sepsis reported globally, 41.5% involve children under the age of five, with 2.9 million deaths associated with the disease. Clinicians must identify and treat patients at risk of sepsis or septic shock before late-stage organ dysfunction occurs since diagnosing sepsis in young patients is more difficult than in adult patients. As of right now, omics technologies that possess adequate diagnostic sensitivity and specificity can assist in locating biomarkers that indicate how the disease will progress clinically and how the patient will react to treatment. By identifying patients who are at a higher risk of dying or experiencing persistent organ dysfunction, risk stratification based on biomarkers generated from proteomics can enhance prognosis. A potentially helpful method for determining the proteins that serve as biomarkers for sepsis and formulating theories on the pathophysiological mechanisms behind complex sepsis symptoms is plasma proteomics.

Acute pain, a critical aspect of patient care, presents a challenge due to its subjective nature and complex biological underpinnings. Biomarkers for acute pain promise a paradigm shift in how pain is perceived, diagnosed, and managed. The study of genetic, inflammatory, and neurotransmission markers associated with pain experience may hold the key for the development of personalized and effective pain management strategies. This narrative review explores the neurobiological pathways of acute pain, encompassing inflammatory responses and neurotransmission mechanisms. It synthesizes current research on the identification and clinical application of biomarkers, emphasizing their potential to enhance diagnostic precision, treatment effectiveness, and risk prediction. We underscore the promising role of acute pain biomarkers in identifying patients at risk for developing acute and potentially chronic pain, predicting patients' response to pharmacological interventions, and aiding in the development of novel therapeutic and pain preventive strategies. The evolving landscape of biomarker research not only deepens our understanding of pain mechanisms but also lays the foundation for more tailored and patient-specific healthcare interventions.

Endometriosis represents a diverse disease characterized by three distinct phenotypes: superficial peritoneal lesions, ovarian endometriomas, and deep infiltrating endometriosis. The most widely accepted pathophysiological hypothesis for endometriosis is rooted in retrograde menstruation, a phenomenon observed in most patients. Endometriosis is closely linked to infertility, but having endometriosis does not necessarily imply infertility. The disease can impact fertility through various mechanisms affecting the pelvic cavity, ovaries, and the uterus itself. MicroRNAs (miRNAs) indeed represent a fascinating and essential component of the regulatory machinery within cells. Discovered in the early 1990s, miRNAs have since been identified as critical players in gene expression control. Unfortunately, ovarian endometrioma is a common gynecologic disorder for which specific serum markers are currently lacking. Some have examined urocortin for its ability to differentiate endometriomas from other benign ovarian cysts. Another potential marker, Cancer Antigen 125 (CA-125) is a well-established indicator for epithelial cell ovarian cancer and its levels can be elevated in conditions such as endometriosis. CA-125 is derived from coelomic epithelia, including the endometrium, fallopian tube, ovary, and peritoneum. In this review we examine the pathophysiologic basis for endometriosis and highlight potential markers to more fully characterize the underlying biochemical processes linked to this multifaceted disease state.

Because of their nature, biomarkers for neuropsychiatric diseases were out of the reach of medical diagnostic technology until the past few decades. In recent years, the confluence of greater, affordable computer power with the need for more efficient diagnoses and treatments has increased interest in and the possibility of their discovery. This review will focus on the progress made over the past ten years regarding the search for electroencephalographic biomarkers for neuropsychiatric diseases. This includes algorithms and methods of analysis, machine learning, and quantitative electroencephalography as applied to neurodegenerative and neurodevelopmental diseases as well as traumatic brain injury and COVID-19. Our findings suggest that there is a need for consensus among quantitative electroencephalography researchers on the classification of biomarkers that most suit this field; that there is a slight disconnection between the development of increasingly sophisticated methods of analysis and what they will actually be of use for in the clinical setting; and finally, that diagnostic biomarkers are the most favored type in the field with a few caveats. The main goal of this state-of-the-art review is to provide the reader with a general panorama of the state of the art in this field.

The present study was conducted to reappraise the prognostic value of decoy receptor 3 (DcR3) for patients with sepsis and septic shock according to the latest Sepsis-3 definitions.

Subjects suffering from sepsis or septic shock were enrolled within 6 h of admission. The Sequential Organ Failure Assessment (SOFA) score and the plasma levels of DcR3, C-reactive protein, procalcitonin, and interleukin-6 were measured. Group comparisons were made based on the survival status on day 28 after onset. Predictors of mortality were assessed using the Cox proportional hazard models, and survival curves were plotted with the Kaplan-Meier method. Discriminative performances of single and combined indicators were evaluated via the areas under receiver operating characteristic curves.

Among 143 eligible sepsis cases, 77 developed septic shock, and the 28-day mortality rates were 32.2% and 45.5%, respectively. Regardless of the population (all sepsis or septic shock), non-survivors exhibited significantly higher DcR3 levels compared to survivors (median 4.19 vs. 2.64 ng/mL and 4.37 vs. 3.18 ng/mL, respectively; p < 0.001 and p = 0.002, respectively). DcR3 levels were most correlated with organ dysfunction presented by SOFA scores (correlation coefficient = 0.347 and 0.308, respectively; p = 0.001 and 0.016, respectively) but did not differ among the various pathogenic microbes of infection. Multivariate Cox regression identified DcR3 as an independent predictor of mortality [hazard ratio (95% confidence interval): 1.570 (1.048-2.352) and 1.828 (1.047-3.194), respectively; p = 0.029 and 0.034, respectively]. Kaplan-Meier analysis showed that elevated DcR3 concentrations were associated with significantly lower survival rates (p = 0.001 and 0.013, respectively). The areas under receiver operating characteristic curves of DcR3 alone for predicting outcome were superior to that of the other three biomarkers (0.731 and 0.711, respectively) and could be further improved when coupled with SOFA scores (0.803 and 0.784, respectively).

DcR3 is a valuable prognostic biomarker for sepsis and septic shock, offering the potential to predict 28-day mortality in clinical settings.

Tumor necrosis factor (TNF) related apoptosis-inducing ligand (TRAIL) is a TNF superfamily cytokine primarily acknowledged for its ability to selectively induce apoptosis in cancer cells. Beyond its antitumor effects, recent literature emphasizes the pleiotropic functions of TRAIL in physiological states and acute/chronic non-malignant diseases indicating its potential to be a breakthrough in diagnostics. This review explores the current understanding of the dynamic role of circulating soluble TRAIL (sTRAIL) and its potential as both a diagnostic and prognostic marker. Multiple in vitro, in vivo, and clinical studies in a wide range of neoplastic and non-neoplastic diseases including infectious diseases have been carried out to explore the potential role of sTRAIL in disease pathogenesis and as well as the possibilities of using it as a diagnostic and prognostic marker. The expression of sTRAIL seems to be context-dependent suggesting further research, particularly towards establishing disease-specific cutoff values. However, the lack of standardization in the serum sTRAIL estimation and the absence of reference intervals remain significant barriers to its clinical application. Addressing these challenges is essential for using circulating sTRAIL as an accurate diagnostic and prognostic marker in clinical practice.

Rheumatic diseases encompass a wide range of autoimmune and inflammatory disorders, including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), psoriatic arthritis (PsA), and systemic sclerosis (SSc). These conditions often result in chronic pain, disability, and reduced quality of life, with unpredictable disease courses that may lead to joint destruction, organ damage, or systemic complications. Biomarkers, defined as measurable indicators of biological processes or conditions, have the potential to transform clinical practice by improving disease diagnosis, monitoring, prognosis, and treatment decisions. While significant strides have been made in identifying and validating biomarkers in rheumatic diseases, challenges remain in their standardization, clinical utility, and integration into routine practice. This review provides an overview of the current state of biomarkers in rheumatic diseases, their roles in clinical settings, and the emerging advancements in the field.

Inflammatory bowel disease (IBD) incidence and prevalence has been increasing worldwide. Limited data exists on the effectiveness of ustekinumab (UST) in children. We aimed to describe the effectiveness and safety of UST in pediatric patients with IBD.

A single-center retrospective study was conducted between January 2017 and February 2022. The study included patients ≤16 years of age who were treated with UST and followed up for ≥1 year. Clinical remission was defined as a score of the Pediatric Crohn's Disease (CD) and Pediatric Ulcerative Colitis (UC) Activity Indices ≤10 at week 52.

Thirteen patients who had failed anti-tumor necrosis factor-α (anti-TNFα) therapy were included, eight (61.5%) with CD and five (38.5%) with UC. The median age was 13 years (interquartile range [IQR]: 11.5 to 14). UST treatment was initiated at a median age of 3 years (IQR: 2.3 to 7) after diagnosis. Ten patients (76.9%) achieved clinical remission. There were no statistically significant differences in characteristics between patients who achieved and did not achieve clinical remission. Biochemical remission (BioR) was achieved in six patients (46.2%). Body mass index (BMI) significantly improved, C-reactive protein (CRP) significantly decreased, and the need for corticosteroids significantly decreased in the remission group. Endoscopy conducted post-treatment in seven patients confirmed remission in six patients. Adverse events included two cases of infection and one of headache.

UST was effective as a secondary biologic therapy for the induction and maintenance of remission in patients with anti-TNFα refractory IBD. At one year, 84% of patients remained on UST with no severe adverse reactions reported.

Disorganisation of inner retinal layers (DRIL) is an optical coherence tomography (OCT) biomarker in patients with diabetic macular oedema. Different ways of quantification of DRIL have been proposed, without final agreement. We found a significant difference in DRIL length if measured with different techniques. Our data highlight that DRIL needs to be measured in a standardised way to provide comparable clinical results. Whereas an international consensus is needed, DRIL measurement as mean of five central OCT scans spaced 120 µm seems better than a single scan and comparable to seven scans, which needs a longer assessment time.

This review explores the intricate landscape of neurodegenerative disease research, focusing on Amyotrophic Lateral Sclerosis (ALS) and the intersection of genetics and RNA biology to investigate the causative pathogenetic basis of this fatal disease. ALS is a severe neurodegenerative disease characterized by the progressive loss of motor neurons, leading to muscle weakness and paralysis. Despite significant research advances, the exact cause of ALS remains largely unknown. Thanks to the application of next-generation sequencing (NGS) approaches, it was possible to highlight the fundamental role of rare variants with large effect sizes and involvement of portions of non-coding RNA, providing valuable information on risk prediction, diagnosis, and treatment of age-related diseases, such as ALS. Genetic research has provided valuable insights into the pathophysiology of ALS, leading to the development of targeted therapies such as antisense oligonucleotides (ASOs). Regulatory agencies in several countries are evaluating the commercialization of Qalsody (Tofersen) for SOD1-associated ALS, highlighting the potential of gene-targeted therapies. Furthermore, the emerging significance of microRNAs (miRNAs) and long RNAs are of great interest. MiRNAs have emerged as promising biomarkers for diagnosing ALS and monitoring disease progression. Understanding the role of lncRNAs in the pathogenesis of ALS opens new avenues for therapeutic intervention. However, challenges remain in delivering RNA-based therapeutics to the central nervous system. Advances in genetic screening and personalized medicine hold promise for improving the management of ALS. Ongoing clinical trials use genomic approaches for patient stratification and drug targeting. Further research into the role of non-coding RNAs in the pathogenesis of ALS and their potential as therapeutic targets is crucial to the development of effective treatments for this devastating disease.

Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, driven by complex interactions among genetic, environmental, and lifestyle factors, with diet playing a pivotal role. Extra Virgin Olive Oil (EVOO), a cornerstone of the Mediterranean diet (MedDiet), is a plant-based fat that has garnered attention for its robust cardiovascular benefits, which are attributed to its unique composition of monounsaturated fatty acids (MUFAs), particularly oleic acid (OA); and bioactive polyphenols, such as Hydroxytyrosol (HT) and oleocanthal. These compounds collectively exert antioxidant, anti-inflammatory, vasodilatory, and lipid-modulating effects. Numerous clinical and preclinical studies have demonstrated that EVOO's properties reduce major modifiable cardiovascular risk factors, including hypertension, dyslipidemia, obesity, and type 2 diabetes. EVOO also promotes endothelial function by increasing nitric oxide (NO) bioavailability, thus favoring vasodilation, lowering blood pressure (BP), and supporting vascular integrity. Furthermore, it modulates biomarkers of cardiovascular health, such as C-reactive protein, low-density lipoprotein (LDL) cholesterol, and NT-proBNP, aligning with improved hemostatic balance and reduced arterial vulnerability. Emerging evidence highlights its interaction with gut microbiota, further augmenting its cardioprotective effects. This review synthesizes current evidence, elucidating EVOO's multifaceted mechanisms of action and therapeutic potential. Future directions emphasize the need for advanced extraction techniques, nutraceutical formulations, and personalized dietary recommendations to maximize its health benefits. EVOO represents a valuable addition to dietary strategies aimed at reducing the global burden of cardiovascular diseases.

An artificial intelligence (AI)-enabled electrocardiogram (ECG) model has been developed in a healthy adult population to predict ECG biological age (ECG-BA). This ECG-BA exhibited a robust correlation with chronological age (CA) in healthy adults and additionally significantly enhanced the prediction of aging-related diseases' onset in adults with subclinical diseases. The model showed particularly strong predictive power for cardiovascular and non-cardiovascular diseases such as stroke, coronary artery disease, peripheral arterial occlusive disease, myocardial infarction, Alzheimer's disease, osteoarthritis, and cancers. When combined with CA, ECG-BA improved diagnostic accuracy and risk classification by 21% over using CA alone, notably offering the greatest improvements in cancer prediction. The net reclassification improvement significantly reduced misclassification rates for disease onset predictions. This comprehensive study validates ECG-BA as an effective supplement to CA, advancing the precision of risk assessments for aging-related conditions and suggesting broad implications for enhancing preventive healthcare strategies, potentially leading to better patient outcomes.

Kidney transplantation stands as the preferred treatment for end-stage kidney disease, significantly improving both the quality and longevity of life compared to dialysis. In recent years, the survival rates for patients and grafts have markedly increased thanks to innovative strategies in desensitization protocols for incompatible transplants and advancements in immunosuppressive therapies. For kidney transplant recipients, preventing allograft rejection is of paramount importance, necessitating the use of immunosuppressive medications. Regular follow-up appointments are essential, as monitoring the function of the kidney allograft is critical. Currently, established biomarkers such as serum creatinine, estimated Glomerular Filtration Rate (eGFR), proteinuria, and albuminuria are commonly employed to assess allograft function. However, these biomarkers have limitations, as elevated levels often indicate significant allograft damage only after it has occurred, thereby constraining treatment options and the potential for restoring graft function. Additionally, kidney biopsies, while considered the gold standard for diagnosing rejection, are invasive and carry associated risks. Consequently, the identification and development of new, sensitive, and specific biomarkers like dd-cfDNA, microRNAs (e.g., miR-21, miR-155), and sCD30 for allograft rejection are crucial. To tackle this challenge, intensive ongoing research employing cutting-edge technologies, including "omics" approaches, like genomic techniques, proteomics, or metabolomics, is uncovering a variety of promising new biomarkers.

Transplant rejection remains a significant barrier to the long-term success of organ transplantation. Biopsy, although considered the gold standard, is invasive, costly, and unsuitable for routine monitoring. Traditional biomarkers, such as creatinine and troponin, offer limited predictive value owing to their low specificity, and conventional imaging techniques often fail to detect early organ damage, increasing the risk of undiagnosed rejection episodes. Considering these limitations, emerging noninvasive biomarkers and molecular imaging techniques hold promise for the early and accurate detection of transplant rejection, enabling personalized management strategies. This review highlights noninvasive biomarkers that predict, diagnose, and assess transplant prognosis by reflecting graft injury, inflammation, and immune responses. For example, donor-derived cell-free DNA (dd-cfDNA) is highly sensitive in detecting early graft injury, whereas gene expression profiling effectively excludes moderate-to-severe acute rejection (AR). Additionally, microRNA (miRNA) profiling enhances the diagnostic specificity for precise AR detection. Advanced molecular imaging techniques further augment the monitoring of rejection. Fluorescence imaging provides a high spatiotemporal resolution for AR grading, ultrasound offers real-time and portable monitoring, and magnetic resonance delivers high tissue contrast for anatomical assessments. Nuclear imaging modalities such as single photon emission computed tomography and positron emission tomography, enable dynamic visualization of immune responses within transplanted organs. Notably, dd-cfDNA and nuclear medicine imaging have already been integrated into clinical practice, thereby demonstrating the translational potential of these techniques. Unlike previous reviews, this work uniquely synthesizes advancements in both noninvasive biomarkers and molecular imaging, emphasizing their complementary strengths. Biomarkers deliver molecular-level insights, whereas imaging provides spatial and temporal resolution. Together, they create a synergistic framework for comprehensive and precise transplant monitoring. By bridging these domains, this review underscores their individual contributions and collective potential to enhance diagnostic accuracy, improve patient outcomes, and guide future research and clinical applications in transplant medicine.

One of the emerging concepts on the reduction of animal use in non-clinical studies is the use of virtual control group (VCG) to replace concurrent control group (CCG). The VCG involves the generation of a control data from historical control data to match a specific study design. This review focuses on two recently published proof-of-concept (POC) studies conducted in rats. One major issue that was consistently seen across these POC studies was the non-reproducibility of some quantitative endpoints between the CCG and the VCG, with clinical pathology parameters being the most affected. The inconsistencies observed with the clinical pathology parameters when using VCGs may lead to: (1) misconception about the accuracy and sensitivity of traditional clinical pathology biomarkers and its implications on safety monitoring in the clinic; (2) inability to correctly identify and characterize organ dysfunctions; (3) interference with the weight-of-evidence approach used in identifying hazards in toxicologic clinical pathology and toxicology studies at large; and (4) wrong interpretations and data reproducibility issues. Other alternatives to reduce animal use in toxicology studies are also discussed including blood microsampling for toxicokinetics, scientifically justified use of recovery animals, and appropriate use and continuous investments in new alternative methods.

Natural medicines (NMs) demonstrate distinct advantages in the clinical management of chronic diseases. Recent years have seen growing recognition of the gut microbiota's role in the efficacy and synergy of NMs, providing new impetus for elucidating the material basis and mechanisms of NMs and their path toward modernization. A fundamental question that has emerged is how NM-microbiota interactions integrate into the multi-target holistic mechanisms of NMs, the answer to which may also illuminate new avenues for drug discovery. Metabolic regulation via small-molecule metabolites has been increasingly implicated in host-microbe interaction. This review presents an integral metabolic perspective on NMs-microbiota interaction in host health and disease. It highlights the emerging understanding of gut microbiota-related metabolic signals implicated in NM components' local and systemic actions. Additionally, it discusses key issues and prospects related to drug development and the translational study of NMs.

Hand osteoarthritis (HOA) is a heterogeneous joint disease with high radiographic and symptomatic prevalence. The diagnosis of HOA is based on clinical and radiographic features. The identification of potential biomarkers for diagnosis, prognosis, disease severity assessment, and therapeutic efficacy evaluation of НОА remains an active area of research. To summarize the eligible biomarker data, a comprehensive narrative review was performed using the PubMed and Scopus databases covering publications from inception to December 2024. Our search uncovered five distinct groups of biomarkers associated with HOA, categorized based on their origin and involvement in distinct biological processes: (1) cartilage synthesis and catabolism, (2) bone remodeling, (3) inflammation, (4) adipokines, and (5) others classified separately. Each biomarker was evaluated in accordance with the Burden of disease, Investigative, Prognostic, Efficacy of intervention, and Diagnostic (BIPED) criteria. In conclusion, no biomarker has yet demonstrated sufficient sensitivity, specificity, or reproducibility to meet the BIPED criteria for classification. The early diagnosis and treatment of HOA require the development of more sensitive assays, advanced platforms, and rigorous bio-clinical trials to stratify previously studied biomarkers and identify novel ones. Precision medicine in HOA demands reliable biomarkers, cost-effective assays, and standardized, reproducible methodologies for global applicability.

Congestion in patients with heart failure (HF) predicts adverse outcomes and is a leading cause of hospitalisation. Understanding congestion mechanisms helps in HF management and underscores the importance of tailored therapies to treat vascular and tissue congestion, improving patient outcomes. In this setting, several tools are available to detect congestion. Biomarker measurement is a simple, valid and affordable method to evaluate congestion in patients with HF. Natriuretic peptides are the most widely available tool in acute and chronic HF, helping diagnosis, risk stratification and management. Novel biomarkers can potentially become reliable allies in diagnosing and monitoring patients with HF. This review aims to assess the current scientific literature on biomarkers for managing HF, evaluate their clinical utility and explore future perspectives in this field.

Multiple sclerosis (MS) is a disorder in which the body's immune system attacks structures of the central nervous system, resulting in lesions that can occur throughout the brain and spinal cord. Cortical lesions, in particular, can contribute to motor dysfunction. Walking disability is reported as the main impairment by people with MS (pwMS), often due to limited ankle movement. This study explored the event-related desynchronization (ERD) onset latency of the sensorimotor rhythms during foot dorsiflexion in pwMS computed using an objective and independent of human criterion method, as an electroencephalogram (EEG) based biomarker. EEG signals were recorded in eight persons with neither neurological condition nor motor dysfunction and eight pwMS with relapsing-remitting, primary progressive or secondary progressive MS. Recordings were divided into three groups: control, more affected lower limb and less affected lower limb. The ERD-onset latency was determined using a method based on the percent of ERD time course and the cluster depth tests. The median and interquartile range of the ERD-onset latency were 1186.0 (1100.0, 1250.0) ms; 1064.0 (1031.0, 1127.0) ms for the more and less affected groups respectively, whereas the median and interquartile range for the control group was 656.0 (472.2, 950.0) ms. There was a significant delay in the ERD-onset latencies of the pwMS groups compared to the control group (p<0.001 for both comparisons). These findings suggest that the ERD-onset latency computed using the proposed method could be used as an EEG biomarker to evaluate disease progression or therapeutic interventions in pwMS.

Ammonia is a product of amino acid metabolism that accumulates in the blood of patients with liver cirrhosis, leading to neurotoxic effects and hepatic encephalopathy (HE). HE manifestations can range from mild, subclinical disturbances in cognition, or minimal HE (mHE) to gross disorientation and coma, a condition referred to as overt HE. Many blood-based biomarkers reflecting these neurotoxic effects of ammonia and liver disease can be measured in the blood allowing the development of new biomarkers to diagnose cirrhosis patients at risk of developing HE. The effect of ammonia on the brain is modulated by severity of systemic inflammation, and both hyperammonemia and inflammation can induce oxidative stress, which may mediate the neurological alterations associated to HE. This review aims to provide the latest evidence on biomarkers of HE beyond ammonia. We present different approaches to predict overt HE based on the combination of blood ammonia with some analytical and clinical parameters. Magnetic resonance analysis of brain images could also provide sensitive diagnostic biomarkers based on neuroimaging parameters. Some reports suggest that markers of systemic inflammation, oxidative stress, and central nervous system-derived components, may serve as additional biomarkers of HE. The involvement of extracellular vesicles and microbiota in the pathophysiology of mHE and HE has recently acquired importance and it would be interesting to explore their usefulness as early biomarkers of the disease. It is important to have a biomarker or a combination of them for early diagnosis of mHE to improve its treatment and prevent progression to overt HE.